class_docstr: Wrapper around tensorflow optimizer.
class_docstr_tok: ["Wrapper","around","tensorflow","optimizer","."]
docstr: Get optimizer hyperparams related to entity and relation embeddings (for partitioned training).
  
  Returns
  -------
  ent_hyperparams: np.array
      Entity embedding related optimizer hyperparameters.
  rel_hyperparams: np.array
      Relation embedding related optimizer hyperparameters.
docstr_tok: ["Get","optimizer","hyperparams","related","to","entity","and","relation","embeddings","(","for","partitioned","training",")",".","Returns","--","--","--","-","ent_hyperparams",":","np.array","Entity","embedding","related","optimizer","hyperparameters",".","rel_hyperparams",":","np.array","Relation","embedding","related","optimizer","hyperparameters","."]
code: def get_entity_relation_hyperparams(self):
      """Get optimizer hyperparams related to entity and relation embeddings (for partitioned training).
  
      Returns
      -------
      ent_hyperparams: np.array
          Entity embedding related optimizer hyperparameters.
      rel_hyperparams: np.array
          Relation embedding related optimizer hyperparameters.
      """
      optim_weights = self.optimizer.get_weights()
      ent_hyperparams = []
      rel_hyperparams = []
      for i in range(1, len(optim_weights), self.num_optimized_vars):
          ent_hyperparams.append(optim_weights[i])
          rel_hyperparams.append(optim_weights[i + 1])
  
      return ent_hyperparams, rel_hyperparams
  
file_path: AmpliGraph/ampligraph/latent_features/optimizers.py
imports: import abc
  import logging
  import six
  import tensorflow
label_desc: Use image node_id 6 for calling the OptimizerWrapper obj's underlying member method code with example usage: obj.get_entity_relation_hyperparams() and returns: ent_hyperparams,  rel_hyperparams